@inproceedings{liu-etal-2022-generated,
title = "Generated Knowledge Prompting for Commonsense Reasoning",
author = "Liu, Jiacheng and
Liu, Alisa and
Lu, Ximing and
Welleck, Sean and
West, Peter and
Le Bras, Ronan and
Choi, Yejin and
Hajishirzi, Hannaneh",
editor = "Muresan, Smaranda and
Nakov, Preslav and
Villavicencio, Aline",
booktitle = "Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = may,
year = "2022",
address = "Dublin, Ireland",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.acl-long.225",
doi = "10.18653/v1/2022.acl-long.225",
pages = "3154--3169",
abstract = "It remains an open question whether incorporating external knowledge benefits commonsense reasoning while maintaining the flexibility of pretrained sequence models. To investigate this question, we develop generated knowledge prompting, which consists of generating knowledge from a language model, then providing the knowledge as additional input when answering a question. Our method does not require task-specific supervision for knowledge integration, or access to a structured knowledge base, yet it improves performance of large-scale, state-of-the-art models on four commonsense reasoning tasks, achieving state-of-the-art results on numerical commonsense (NumerSense), general commonsense (CommonsenseQA 2.0), and scientific commonsense (QASC) benchmarks. Generated knowledge prompting highlights large-scale language models as flexible sources of external knowledge for improving commonsense reasoning. Our code is available at \url{github.com/liujch1998/GKP}",
}
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<abstract>It remains an open question whether incorporating external knowledge benefits commonsense reasoning while maintaining the flexibility of pretrained sequence models. To investigate this question, we develop generated knowledge prompting, which consists of generating knowledge from a language model, then providing the knowledge as additional input when answering a question. Our method does not require task-specific supervision for knowledge integration, or access to a structured knowledge base, yet it improves performance of large-scale, state-of-the-art models on four commonsense reasoning tasks, achieving state-of-the-art results on numerical commonsense (NumerSense), general commonsense (CommonsenseQA 2.0), and scientific commonsense (QASC) benchmarks. Generated knowledge prompting highlights large-scale language models as flexible sources of external knowledge for improving commonsense reasoning. Our code is available at github.com/liujch1998/GKP</abstract>
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%0 Conference Proceedings
%T Generated Knowledge Prompting for Commonsense Reasoning
%A Liu, Jiacheng
%A Liu, Alisa
%A Lu, Ximing
%A Welleck, Sean
%A West, Peter
%A Le Bras, Ronan
%A Choi, Yejin
%A Hajishirzi, Hannaneh
%Y Muresan, Smaranda
%Y Nakov, Preslav
%Y Villavicencio, Aline
%S Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2022
%8 May
%I Association for Computational Linguistics
%C Dublin, Ireland
%F liu-etal-2022-generated
%X It remains an open question whether incorporating external knowledge benefits commonsense reasoning while maintaining the flexibility of pretrained sequence models. To investigate this question, we develop generated knowledge prompting, which consists of generating knowledge from a language model, then providing the knowledge as additional input when answering a question. Our method does not require task-specific supervision for knowledge integration, or access to a structured knowledge base, yet it improves performance of large-scale, state-of-the-art models on four commonsense reasoning tasks, achieving state-of-the-art results on numerical commonsense (NumerSense), general commonsense (CommonsenseQA 2.0), and scientific commonsense (QASC) benchmarks. Generated knowledge prompting highlights large-scale language models as flexible sources of external knowledge for improving commonsense reasoning. Our code is available at github.com/liujch1998/GKP
%R 10.18653/v1/2022.acl-long.225
%U https://aclanthology.org/2022.acl-long.225
%U https://doi.org/10.18653/v1/2022.acl-long.225
%P 3154-3169
Markdown (Informal)
[Generated Knowledge Prompting for Commonsense Reasoning](https://aclanthology.org/2022.acl-long.225) (Liu et al., ACL 2022)
ACL
- Jiacheng Liu, Alisa Liu, Ximing Lu, Sean Welleck, Peter West, Ronan Le Bras, Yejin Choi, and Hannaneh Hajishirzi. 2022. Generated Knowledge Prompting for Commonsense Reasoning. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 3154–3169, Dublin, Ireland. Association for Computational Linguistics.